Overview

Dataset statistics

Number of variables26
Number of observations83084
Missing cells913242
Missing cells (%)42.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory50.6 MiB
Average record size in memory639.2 B

Variable types

Categorical8
Numeric18

Warnings

SUBJ_DESC has a high cardinality: 73 distinct values High cardinality
PSC is highly correlated with PLC and 1 other fieldsHigh correlation
PMA is highly correlated with PCNHigh correlation
PLC is highly correlated with PSCHigh correlation
PCN is highly correlated with PSC and 1 other fieldsHigh correlation
ICQU is highly correlated with ICMA and 4 other fieldsHigh correlation
ICMA is highly correlated with ICQU and 2 other fieldsHigh correlation
ICLE is highly correlated with ICFL and 1 other fieldsHigh correlation
ICHI is highly correlated with ICQU and 1 other fieldsHigh correlation
ICGE is highly correlated with ICHIHigh correlation
ICFL is highly correlated with ICQU and 1 other fieldsHigh correlation
ICFI is highly correlated with ICQU and 3 other fieldsHigh correlation
ICBI is highly correlated with ICQU and 3 other fieldsHigh correlation
ICSO is highly correlated with ICBIHigh correlation
PSC is highly correlated with PLCHigh correlation
PMA is highly correlated with PCNHigh correlation
PLC is highly correlated with PSCHigh correlation
PCN is highly correlated with PMAHigh correlation
ICQU is highly correlated with ICMA and 4 other fieldsHigh correlation
ICMA is highly correlated with ICQU and 2 other fieldsHigh correlation
ICLE is highly correlated with ICFL and 1 other fieldsHigh correlation
ICHI is highly correlated with ICQU and 1 other fieldsHigh correlation
ICGE is highly correlated with ICQU and 1 other fieldsHigh correlation
ICFL is highly correlated with ICLEHigh correlation
ICFI is highly correlated with ICQU and 4 other fieldsHigh correlation
ICBI is highly correlated with ICQU and 3 other fieldsHigh correlation
ICSO is highly correlated with ICFI and 1 other fieldsHigh correlation
ICHI is highly correlated with ICGEHigh correlation
ICGE is highly correlated with ICHIHigh correlation
IND_REP is highly correlated with GRDE_CODE_FINALHigh correlation
MAJR_DESC1 is highly correlated with ICSO and 8 other fieldsHigh correlation
ICMA is highly correlated with ICFL and 7 other fieldsHigh correlation
PLC is highly correlated with PMA and 2 other fieldsHigh correlation
ICFL is highly correlated with ICMA and 7 other fieldsHigh correlation
PIN is highly correlated with PMA and 1 other fieldsHigh correlation
ICSO is highly correlated with MAJR_DESC1 and 6 other fieldsHigh correlation
ICGE is highly correlated with MAJR_DESC1 and 10 other fieldsHigh correlation
HAVE_PHONE is highly correlated with ICGE and 1 other fieldsHigh correlation
CRN_KEY is highly correlated with SUBJ_DESC and 2 other fieldsHigh correlation
ICBI is highly correlated with ICMA and 7 other fieldsHigh correlation
GRDE_CODE_FINAL is highly correlated with IND_REP and 1 other fieldsHigh correlation
HAVE_BECA is highly correlated with PCN and 1 other fieldsHigh correlation
PMA is highly correlated with PLC and 3 other fieldsHigh correlation
ICQU is highly correlated with MAJR_DESC1 and 9 other fieldsHigh correlation
CRED_ACAD is highly correlated with SUBJ_DESCHigh correlation
CRSE_NUMBER is highly correlated with MAJR_DESC1 and 2 other fieldsHigh correlation
PSC is highly correlated with PLC and 2 other fieldsHigh correlation
ICLE is highly correlated with ICMA and 7 other fieldsHigh correlation
SUBJ_DESC is highly correlated with MAJR_DESC1 and 5 other fieldsHigh correlation
ICHI is highly correlated with MAJR_DESC1 and 13 other fieldsHigh correlation
COLL_DESC is highly correlated with MAJR_DESC1 and 6 other fieldsHigh correlation
PCN is highly correlated with PLC and 4 other fieldsHigh correlation
CAMPUS is highly correlated with MAJR_DESC1 and 3 other fieldsHigh correlation
ICFI is highly correlated with MAJR_DESC1 and 8 other fieldsHigh correlation
BECA_GOB_ICFES is highly correlated with HAVE_BECA and 1 other fieldsHigh correlation
COLL_DESC is highly correlated with MAJR_DESC1High correlation
MAJR_DESC1 is highly correlated with COLL_DESC and 1 other fieldsHigh correlation
CAMPUS is highly correlated with MAJR_DESC1High correlation
HAVE_BECA is highly correlated with BECA_GOB_ICFESHigh correlation
BECA_GOB_ICFES is highly correlated with HAVE_BECAHigh correlation
IND_REP has 75153 (90.5%) missing values Missing
PSC has 26574 (32.0%) missing values Missing
PMA has 26574 (32.0%) missing values Missing
PLC has 26574 (32.0%) missing values Missing
PIN has 26574 (32.0%) missing values Missing
PCN has 26610 (32.0%) missing values Missing
ICQU has 77050 (92.7%) missing values Missing
ICMA has 77050 (92.7%) missing values Missing
ICLE has 77050 (92.7%) missing values Missing
ICHI has 82749 (99.6%) missing values Missing
ICGE has 82749 (99.6%) missing values Missing
ICFL has 77050 (92.7%) missing values Missing
ICFI has 77050 (92.7%) missing values Missing
ICBI has 77050 (92.7%) missing values Missing
ICSO has 77385 (93.1%) missing values Missing
CRED_ACAD has 2178 (2.6%) zeros Zeros

Reproduction

Analysis started2021-06-16 04:32:46.865324
Analysis finished2021-06-16 04:34:41.878612
Duration1 minute and 55.01 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

SUBJ_DESC
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct73
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.4 MiB
MATEMATICAS
9670 
DISEÑO
8603 
COMPUTACION
5256 
FISICA
5055 
SOCIOLOGIA
 
4476
Other values (68)
50024 

Length

Max length30
Median length11
Mean length12.22193202
Min length6

Characters and Unicode

Total characters1015447
Distinct characters31
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowADMINISTRACION
2nd rowADMINISTRACION
3rd rowADMINISTRACION
4th rowADMINISTRACION
5th rowADMINISTRACION

Common Values

ValueCountFrequency (%)
MATEMATICAS9670
 
11.6%
DISEÑO8603
 
10.4%
COMPUTACION5256
 
6.3%
FISICA5055
 
6.1%
SOCIOLOGIA4476
 
5.4%
FINANZAS CORPORATIVAS3710
 
4.5%
SISTEMICA2890
 
3.5%
BIOMEDICA2839
 
3.4%
AUTOMATIZACION Y CONTROL2724
 
3.3%
TERMOFLUIDOS2300
 
2.8%
Other values (63)35561
42.8%

Length

2021-06-15T23:34:42.075642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
matematicas9670
 
8.5%
diseño8603
 
7.5%
y8489
 
7.4%
computacion5256
 
4.6%
fisica5055
 
4.4%
sociologia4476
 
3.9%
finanzas3710
 
3.3%
corporativas3710
 
3.3%
energia2944
 
2.6%
sistemica2890
 
2.5%
Other values (91)59161
51.9%

Most occurring characters

ValueCountFrequency (%)
I127896
12.6%
A125655
12.4%
O99321
9.8%
C89873
8.9%
E83365
8.2%
T70254
 
6.9%
S64244
 
6.3%
N57201
 
5.6%
M49535
 
4.9%
R43924
 
4.3%
Other values (21)204179
20.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter984391
96.9%
Space Separator30880
 
3.0%
Other Punctuation174
 
< 0.1%
Decimal Number2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I127896
13.0%
A125655
12.8%
O99321
10.1%
C89873
9.1%
E83365
8.5%
T70254
7.1%
S64244
 
6.5%
N57201
 
5.8%
M49535
 
5.0%
R43924
 
4.5%
Other values (17)173123
17.6%
Decimal Number
ValueCountFrequency (%)
31
50.0%
11
50.0%
Space Separator
ValueCountFrequency (%)
30880
100.0%
Other Punctuation
ValueCountFrequency (%)
.174
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin984391
96.9%
Common31056
 
3.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
I127896
13.0%
A125655
12.8%
O99321
10.1%
C89873
9.1%
E83365
8.5%
T70254
7.1%
S64244
 
6.5%
N57201
 
5.8%
M49535
 
5.0%
R43924
 
4.5%
Other values (17)173123
17.6%
Common
ValueCountFrequency (%)
30880
99.4%
.174
 
0.6%
31
 
< 0.1%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1006844
99.2%
Latin 1 Sup8603
 
0.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I127896
12.7%
A125655
12.5%
O99321
9.9%
C89873
8.9%
E83365
8.3%
T70254
 
7.0%
S64244
 
6.4%
N57201
 
5.7%
M49535
 
4.9%
R43924
 
4.4%
Other values (20)195576
19.4%
Latin 1 Sup
ValueCountFrequency (%)
Ñ8603
100.0%

CRSE_NUMBER
Real number (ℝ≥0)

HIGH CORRELATION

Distinct199
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10183.59626
Minimum101
Maximum26013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2021-06-15T23:34:42.181614image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1105
median15009
Q318006
95-th percentile19003
Maximum26013
Range25912
Interquartile range (IQR)17901

Descriptive statistics

Standard deviation8839.704617
Coefficient of variation (CV)0.8680336875
Kurtosis-1.888118253
Mean10183.59626
Median Absolute Deviation (MAD)3993
Skewness-0.2295100956
Sum846093912
Variance78140377.71
MonotonicityNot monotonic
2021-06-15T23:34:42.293641image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1018100
 
9.7%
180017534
 
9.1%
180025158
 
6.2%
190014738
 
5.7%
1024229
 
5.1%
1033972
 
4.8%
1043182
 
3.8%
180032435
 
2.9%
1072365
 
2.8%
190032085
 
2.5%
Other values (189)39286
47.3%
ValueCountFrequency (%)
1018100
9.7%
1024229
5.1%
1033972
4.8%
1043182
 
3.8%
1052006
 
2.4%
1061537
 
1.8%
1072365
 
2.8%
108765
 
0.9%
1091042
 
1.3%
1101126
 
1.4%
ValueCountFrequency (%)
260131
 
< 0.1%
260031
 
< 0.1%
260023
 
< 0.1%
2500528
 
< 0.1%
2500460
 
0.1%
25002265
0.3%
220012
 
< 0.1%
216101
 
< 0.1%
1903257
 
0.1%
1903159
 
0.1%

GRDE_CODE_FINAL
Real number (ℝ≥0)

HIGH CORRELATION

Distinct51
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.771265226
Minimum0
Maximum5
Zeros477
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2021-06-15T23:34:42.405672image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.6
Q13.3
median3.8
Q34.3
95-th percentile4.8
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7748867099
Coefficient of variation (CV)0.2054712845
Kurtosis4.052734555
Mean3.771265226
Median Absolute Deviation (MAD)0.5
Skewness-1.333337753
Sum313331.8
Variance0.6004494132
MonotonicityNot monotonic
2021-06-15T23:34:42.512643image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35383
 
6.5%
44725
 
5.7%
4.14463
 
5.4%
4.24374
 
5.3%
4.34236
 
5.1%
3.84141
 
5.0%
3.94085
 
4.9%
3.73990
 
4.8%
4.43987
 
4.8%
4.53924
 
4.7%
Other values (41)39776
47.9%
ValueCountFrequency (%)
0477
0.6%
0.142
 
0.1%
0.244
 
0.1%
0.335
 
< 0.1%
0.429
 
< 0.1%
0.5105
 
0.1%
0.651
 
0.1%
0.759
 
0.1%
0.849
 
0.1%
0.972
 
0.1%
ValueCountFrequency (%)
52474
3.0%
4.91445
 
1.7%
4.82007
2.4%
4.72568
3.1%
4.63151
3.8%
4.53924
4.7%
4.43987
4.8%
4.34236
5.1%
4.24374
5.3%
4.14463
5.4%

CRN_KEY
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2631
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46767.03765
Minimum35069
Maximum55535
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2021-06-15T23:34:42.621670image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum35069
5-th percentile36439
Q143419
median47801.5
Q350698
95-th percentile54234
Maximum55535
Range20466
Interquartile range (IQR)7279

Descriptive statistics

Standard deviation5271.029187
Coefficient of variation (CV)0.1127082118
Kurtosis-0.6495261632
Mean46767.03765
Median Absolute Deviation (MAD)3181.5
Skewness-0.5031430672
Sum3885592556
Variance27783748.69
MonotonicityNot monotonic
2021-06-15T23:34:42.723670image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37717269
 
0.3%
42066267
 
0.3%
49664245
 
0.3%
37714233
 
0.3%
35942229
 
0.3%
44840227
 
0.3%
40423225
 
0.3%
37028222
 
0.3%
40421219
 
0.3%
50428215
 
0.3%
Other values (2621)80733
97.2%
ValueCountFrequency (%)
35069195
0.2%
350766
 
< 0.1%
35311124
0.1%
35332174
0.2%
3534349
 
0.1%
353505
 
< 0.1%
353542
 
< 0.1%
3536087
0.1%
3536313
 
< 0.1%
3536611
 
< 0.1%
ValueCountFrequency (%)
555351
 
< 0.1%
555341
 
< 0.1%
555254
 
< 0.1%
555234
 
< 0.1%
555161
 
< 0.1%
554979
< 0.1%
554941
 
< 0.1%
554751
 
< 0.1%
554669
< 0.1%
5546517
< 0.1%

CRED_ACAD
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.943779789
Minimum0
Maximum16
Zeros2178
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2021-06-15T23:34:42.815670image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile4
Maximum16
Range16
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.496246625
Coefficient of variation (CV)0.5082739647
Kurtosis33.60205399
Mean2.943779789
Median Absolute Deviation (MAD)1
Skewness3.86010005
Sum244581
Variance2.238753962
MonotonicityNot monotonic
2021-06-15T23:34:42.895669image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
336652
44.1%
421485
25.9%
212452
 
15.0%
18648
 
10.4%
02178
 
2.6%
5579
 
0.7%
16504
 
0.6%
8317
 
0.4%
6265
 
0.3%
122
 
< 0.1%
Other values (2)2
 
< 0.1%
ValueCountFrequency (%)
02178
 
2.6%
18648
 
10.4%
212452
 
15.0%
336652
44.1%
421485
25.9%
5579
 
0.7%
6265
 
0.3%
8317
 
0.4%
101
 
< 0.1%
122
 
< 0.1%
ValueCountFrequency (%)
16504
 
0.6%
141
 
< 0.1%
122
 
< 0.1%
101
 
< 0.1%
8317
 
0.4%
6265
 
0.3%
5579
 
0.7%
421485
25.9%
336652
44.1%
212452
 
15.0%

COLL_DESC
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.1 MiB
FAC DE INGENIERIA
27906 
FAC DE INGEN FISICO MECANICAS
27184 
FAC DE INGEN ADMINISTRATIVAS
14880 
FAC DE INGEN DE SISTEMAS
13114 

Length

Max length29
Median length28
Mean length24.00117953
Min length17

Characters and Unicode

Total characters1994114
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFAC DE INGEN ADMINISTRATIVAS
2nd rowFAC DE INGEN ADMINISTRATIVAS
3rd rowFAC DE INGEN ADMINISTRATIVAS
4th rowFAC DE INGEN ADMINISTRATIVAS
5th rowFAC DE INGEN ADMINISTRATIVAS

Common Values

ValueCountFrequency (%)
FAC DE INGENIERIA27906
33.6%
FAC DE INGEN FISICO MECANICAS27184
32.7%
FAC DE INGEN ADMINISTRATIVAS14880
17.9%
FAC DE INGEN DE SISTEMAS13114
15.8%

Length

2021-06-15T23:34:43.112643image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-15T23:34:43.181672image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
de96198
27.9%
fac83084
24.1%
ingen55178
16.0%
ingenieria27906
 
8.1%
mecanicas27184
 
7.9%
fisico27184
 
7.9%
administrativas14880
 
4.3%
sistemas13114
 
3.8%

Most occurring characters

ValueCountFrequency (%)
I278202
14.0%
261644
13.1%
E247486
12.4%
A223112
11.2%
N208232
10.4%
C164636
8.3%
S123470
6.2%
D111078
 
5.6%
F110268
 
5.5%
G83084
 
4.2%
Other values (5)182902
9.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1732470
86.9%
Space Separator261644
 
13.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I278202
16.1%
E247486
14.3%
A223112
12.9%
N208232
12.0%
C164636
9.5%
S123470
7.1%
D111078
 
6.4%
F110268
 
6.4%
G83084
 
4.8%
M55178
 
3.2%
Other values (4)127724
7.4%
Space Separator
ValueCountFrequency (%)
261644
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1732470
86.9%
Common261644
 
13.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
I278202
16.1%
E247486
14.3%
A223112
12.9%
N208232
12.0%
C164636
9.5%
S123470
7.1%
D111078
 
6.4%
F110268
 
6.4%
G83084
 
4.8%
M55178
 
3.2%
Other values (4)127724
7.4%
Common
ValueCountFrequency (%)
261644
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1994114
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I278202
14.0%
261644
13.1%
E247486
12.4%
A223112
11.2%
N208232
10.4%
C164636
8.3%
S123470
6.2%
D111078
 
5.6%
F110268
 
5.5%
G83084
 
4.2%
Other values (5)182902
9.2%

MAJR_DESC1
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.8 MiB
INGENIERIA MECATRONICA
19452 
INGENIERIA DE SISTEMAS
18104 
INGENIERIA FINANCIERA
15251 
INGENIERIA EN ENERGIA
12083 
INGENIERIA BIOMEDICA
10671 
Other values (3)
7523 

Length

Max length29
Median length22
Mean length21.40333879
Min length20

Characters and Unicode

Total characters1778275
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINGENIERIA DE MERCADOS
2nd rowINGENIERIA DE MERCADOS
3rd rowINGENIERIA DE MERCADOS
4th rowINGENIERIA DE MERCADOS
5th rowINGENIERIA DE MERCADOS

Common Values

ValueCountFrequency (%)
INGENIERIA MECATRONICA19452
23.4%
INGENIERIA DE SISTEMAS18104
21.8%
INGENIERIA FINANCIERA15251
18.4%
INGENIERIA EN ENERGIA12083
14.5%
INGENIERIA BIOMEDICA10671
12.8%
INGENIERIA DE MERCADOS4994
 
6.0%
INGENIERIA INDUSTRIAL2325
 
2.8%
GESTION DE SISTEMAS DE INFORM204
 
0.2%

Length

2021-06-15T23:34:43.397643image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-15T23:34:43.468670image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
ingenieria82880
41.0%
de23506
 
11.6%
mecatronica19452
 
9.6%
sistemas18308
 
9.1%
financiera15251
 
7.6%
energia12083
 
6.0%
en12083
 
6.0%
biomedica10671
 
5.3%
mercados4994
 
2.5%
industrial2325
 
1.2%
Other values (2)408
 
0.2%

Most occurring characters

ValueCountFrequency (%)
I355385
20.0%
E294395
16.6%
N242613
13.6%
A200667
11.3%
R137189
 
7.7%
118877
 
6.7%
G95167
 
5.4%
C69820
 
3.9%
S62447
 
3.5%
M53629
 
3.0%
Other values (7)148086
8.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1659398
93.3%
Space Separator118877
 
6.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I355385
21.4%
E294395
17.7%
N242613
14.6%
A200667
12.1%
R137189
 
8.3%
G95167
 
5.7%
C69820
 
4.2%
S62447
 
3.8%
M53629
 
3.2%
D41496
 
2.5%
Other values (6)106590
 
6.4%
Space Separator
ValueCountFrequency (%)
118877
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1659398
93.3%
Common118877
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
I355385
21.4%
E294395
17.7%
N242613
14.6%
A200667
12.1%
R137189
 
8.3%
G95167
 
5.7%
C69820
 
4.2%
S62447
 
3.8%
M53629
 
3.2%
D41496
 
2.5%
Other values (6)106590
 
6.4%
Common
ValueCountFrequency (%)
118877
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1778275
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I355385
20.0%
E294395
16.6%
N242613
13.6%
A200667
11.3%
R137189
 
7.7%
118877
 
6.7%
G95167
 
5.4%
C69820
 
3.9%
S62447
 
3.5%
M53629
 
3.0%
Other values (7)148086
8.3%

CAMPUS
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.3 MiB
CC
82876 
CV
 
204
UN
 
4

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters166168
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCC
2nd rowCC
3rd rowCC
4th rowCC
5th rowCC

Common Values

ValueCountFrequency (%)
CC82876
99.7%
CV204
 
0.2%
UN4
 
< 0.1%

Length

2021-06-15T23:34:43.704679image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-15T23:34:43.770223image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
cc82876
99.7%
cv204
 
0.2%
un4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
C165956
99.9%
V204
 
0.1%
U4
 
< 0.1%
N4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter166168
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C165956
99.9%
V204
 
0.1%
U4
 
< 0.1%
N4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin166168
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C165956
99.9%
V204
 
0.1%
U4
 
< 0.1%
N4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII166168
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C165956
99.9%
V204
 
0.1%
U4
 
< 0.1%
N4
 
< 0.1%

IND_REP
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing75153
Missing (%)90.5%
Memory size3.4 MiB
E
4244 
I
3687 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7931
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE
2nd rowE
3rd rowE
4th rowI
5th rowI

Common Values

ValueCountFrequency (%)
E4244
 
5.1%
I3687
 
4.4%
(Missing)75153
90.5%

Length

2021-06-15T23:34:43.937253image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-15T23:34:44.000254image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
e4244
53.5%
i3687
46.5%

Most occurring characters

ValueCountFrequency (%)
E4244
53.5%
I3687
46.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter7931
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E4244
53.5%
I3687
46.5%

Most occurring scripts

ValueCountFrequency (%)
Latin7931
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E4244
53.5%
I3687
46.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII7931
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E4244
53.5%
I3687
46.5%

BECA_GOB_ICFES
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.9 MiB
NOTIENE
49521 
BI14 - PLAN 10000 BECAS GOBIERNO 2014, BM14 - MATRICULA 10000 BECAS GOB 2014
12455 
BI15 - PLAN 10000 BECAS GOBIERNO 2015, BM15 - MATRICULA 10000 BECAS GOB 2015
11143 
BI16 - PLAN 10000 BECAS GOBIERNO 2016, BM16 - MATRICULA 10000 BECAS GOB 2016
5381 
BI17 - PLAN 10000 BECAS GOBIERNO 2017, BM17 - MATRICULA 10000 BECAS GOB 2017
 
3006
Other values (11)
 
1578

Length

Max length77
Median length7
Mean length34.78703481
Min length7

Characters and Unicode

Total characters2890246
Distinct characters27
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNOTIENE
2nd rowNOTIENE
3rd rowNOTIENE
4th rowNOTIENE
5th rowNOTIENE

Common Values

ValueCountFrequency (%)
NOTIENE49521
59.6%
BI14 - PLAN 10000 BECAS GOBIERNO 2014, BM14 - MATRICULA 10000 BECAS GOB 201412455
 
15.0%
BI15 - PLAN 10000 BECAS GOBIERNO 2015, BM15 - MATRICULA 10000 BECAS GOB 201511143
 
13.4%
BI16 - PLAN 10000 BECAS GOBIERNO 2016, BM16 - MATRICULA 10000 BECAS GOB 20165381
 
6.5%
BI17 - PLAN 10000 BECAS GOBIERNO 2017, BM17 - MATRICULA 10000 BECAS GOB 20173006
 
3.6%
BI18 - GENERACION E BECAS GOB 2018, BM18 - MATRICULA GEN E BECAS GOB 2018599
 
0.7%
BI19 - GENERACION E BECAS GOB 2019361
 
0.4%
BI17 - PLAN 10000 BECAS GOBIERNO 2017183
 
0.2%
BM14 - MATRICULA 10000 BECAS GOB 2014132
 
0.2%
BM15 - MATRICULA 10000 BECAS GOB 201574
 
0.1%
Other values (6)229
 
0.3%

Length

2021-06-15T23:34:44.173253image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
66157
12.9%
becas66157
12.9%
1000064571
12.6%
notiene49521
9.6%
gob33827
 
6.6%
matricula32840
 
6.4%
plan32330
 
6.3%
gobierno32330
 
6.3%
201425079
 
4.9%
201522435
 
4.4%
Other values (18)87972
17.1%

Most occurring characters

ValueCountFrequency (%)
463698
16.0%
0324441
11.2%
E201688
 
7.0%
B198471
 
6.9%
1196885
 
6.8%
N166275
 
5.8%
A165154
 
5.7%
O148995
 
5.2%
I148995
 
5.2%
C99984
 
3.5%
Other values (17)775660
26.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1608000
55.6%
Decimal Number719797
24.9%
Space Separator463698
 
16.0%
Dash Punctuation66157
 
2.3%
Other Punctuation32594
 
1.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E201688
12.5%
B198471
12.3%
N166275
10.3%
A165154
10.3%
O148995
9.3%
I148995
9.3%
C99984
 
6.2%
T82361
 
5.1%
G67743
 
4.2%
S66157
 
4.1%
Other values (5)262177
16.3%
Decimal Number
ValueCountFrequency (%)
0324441
45.1%
1196885
27.4%
266157
 
9.2%
450158
 
7.0%
544870
 
6.2%
621724
 
3.0%
712390
 
1.7%
82450
 
0.3%
9722
 
0.1%
Space Separator
ValueCountFrequency (%)
463698
100.0%
Dash Punctuation
ValueCountFrequency (%)
-66157
100.0%
Other Punctuation
ValueCountFrequency (%)
,32594
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1608000
55.6%
Common1282246
44.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
E201688
12.5%
B198471
12.3%
N166275
10.3%
A165154
10.3%
O148995
9.3%
I148995
9.3%
C99984
 
6.2%
T82361
 
5.1%
G67743
 
4.2%
S66157
 
4.1%
Other values (5)262177
16.3%
Common
ValueCountFrequency (%)
463698
36.2%
0324441
25.3%
1196885
15.4%
-66157
 
5.2%
266157
 
5.2%
450158
 
3.9%
544870
 
3.5%
,32594
 
2.5%
621724
 
1.7%
712390
 
1.0%
Other values (2)3172
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2890246
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
463698
16.0%
0324441
11.2%
E201688
 
7.0%
B198471
 
6.9%
1196885
 
6.8%
N166275
 
5.8%
A165154
 
5.7%
O148995
 
5.2%
I148995
 
5.2%
C99984
 
3.5%
Other values (17)775660
26.8%

HAVE_BECA
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.2 MiB
0
49521 
1
33563 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters83084
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
049521
59.6%
133563
40.4%

Length

2021-06-15T23:34:44.376178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-15T23:34:44.440176image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
049521
59.6%
133563
40.4%

Most occurring characters

ValueCountFrequency (%)
049521
59.6%
133563
40.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number83084
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
049521
59.6%
133563
40.4%

Most occurring scripts

ValueCountFrequency (%)
Common83084
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
049521
59.6%
133563
40.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII83084
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
049521
59.6%
133563
40.4%

HAVE_PHONE
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.2 MiB
0
47344 
1
35740 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters83084
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
047344
57.0%
135740
43.0%

Length

2021-06-15T23:34:44.596179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-15T23:34:44.659178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
047344
57.0%
135740
43.0%

Most occurring characters

ValueCountFrequency (%)
047344
57.0%
135740
43.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number83084
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
047344
57.0%
135740
43.0%

Most occurring scripts

ValueCountFrequency (%)
Common83084
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
047344
57.0%
135740
43.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII83084
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
047344
57.0%
135740
43.0%

PSC
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct54
Distinct (%)0.1%
Missing26574
Missing (%)32.0%
Infinite0
Infinite (%)0.0%
Mean64.6757919
Minimum31
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2021-06-15T23:34:44.730178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile52
Q160
median65
Q370
95-th percentile76
Maximum100
Range69
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.626750404
Coefficient of variation (CV)0.1179227989
Kurtosis0.8802472113
Mean64.6757919
Median Absolute Deviation (MAD)5
Skewness-0.2882699828
Sum3654829
Variance58.16732172
MonotonicityNot monotonic
2021-06-15T23:34:44.834176image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
654512
 
5.4%
623920
 
4.7%
673493
 
4.2%
703329
 
4.0%
593034
 
3.7%
682798
 
3.4%
642772
 
3.3%
722326
 
2.8%
692164
 
2.6%
612146
 
2.6%
Other values (44)26016
31.3%
(Missing)26574
32.0%
ValueCountFrequency (%)
3122
 
< 0.1%
354
 
< 0.1%
3649
 
0.1%
375
 
< 0.1%
3818
 
< 0.1%
3954
 
0.1%
4047
 
0.1%
4165
0.1%
42138
0.2%
4379
0.1%
ValueCountFrequency (%)
10038
 
< 0.1%
88173
 
0.2%
8752
 
0.1%
84105
 
0.1%
8353
 
0.1%
8234
 
< 0.1%
81500
0.6%
8036
 
< 0.1%
79408
0.5%
78534
0.6%

PMA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct56
Distinct (%)0.1%
Missing26574
Missing (%)32.0%
Infinite0
Infinite (%)0.0%
Mean69.79768183
Minimum36
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2021-06-15T23:34:44.950176image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile55
Q164
median70
Q375
95-th percentile84
Maximum100
Range64
Interquartile range (IQR)11

Descriptive statistics

Standard deviation9.130150053
Coefficient of variation (CV)0.1308087864
Kurtosis0.7248443497
Mean69.79768183
Median Absolute Deviation (MAD)6
Skewness0.06599426326
Sum3944267
Variance83.35963999
MonotonicityNot monotonic
2021-06-15T23:34:45.051175image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
703838
 
4.6%
743754
 
4.5%
723610
 
4.3%
673388
 
4.1%
632305
 
2.8%
752298
 
2.8%
642292
 
2.8%
682148
 
2.6%
652050
 
2.5%
691920
 
2.3%
Other values (46)28907
34.8%
(Missing)26574
32.0%
ValueCountFrequency (%)
3624
 
< 0.1%
3851
 
0.1%
393
 
< 0.1%
4216
 
< 0.1%
44121
0.1%
45114
0.1%
46221
0.3%
47218
0.3%
4898
0.1%
49186
0.2%
ValueCountFrequency (%)
100388
0.5%
9738
 
< 0.1%
96168
 
0.2%
9556
 
0.1%
93184
 
0.2%
9248
 
0.1%
90151
 
0.2%
89275
0.3%
87678
0.8%
86194
 
0.2%

PLC
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct48
Distinct (%)0.1%
Missing26574
Missing (%)32.0%
Infinite0
Infinite (%)0.0%
Mean63.72436737
Minimum40
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2021-06-15T23:34:45.160177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile52
Q159
median63
Q369
95-th percentile76
Maximum93
Range53
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.30625664
Coefficient of variation (CV)0.1146540474
Kurtosis0.1448315384
Mean63.72436737
Median Absolute Deviation (MAD)5
Skewness0.02645706064
Sum3601064
Variance53.38138608
MonotonicityNot monotonic
2021-06-15T23:34:45.267177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
655482
 
6.6%
634442
 
5.3%
674160
 
5.0%
693527
 
4.2%
603504
 
4.2%
613207
 
3.9%
582623
 
3.2%
572596
 
3.1%
542353
 
2.8%
712337
 
2.8%
Other values (38)22279
26.8%
(Missing)26574
32.0%
ValueCountFrequency (%)
4027
 
< 0.1%
4125
 
< 0.1%
4232
 
< 0.1%
4356
 
0.1%
44137
 
0.2%
4536
 
< 0.1%
46289
0.3%
47323
0.4%
48246
0.3%
49492
0.6%
ValueCountFrequency (%)
9354
 
0.1%
9223
 
< 0.1%
8518
 
< 0.1%
84163
0.2%
8395
 
0.1%
82159
0.2%
8160
 
0.1%
80263
0.3%
79338
0.4%
78394
0.5%

PIN
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct62
Distinct (%)0.1%
Missing26574
Missing (%)32.0%
Infinite0
Infinite (%)0.0%
Mean63.92590692
Minimum35
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2021-06-15T23:34:45.377148image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile46
Q155
median63
Q372
95-th percentile83
Maximum100
Range65
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.6049232
Coefficient of variation (CV)0.1815370913
Kurtosis-0.01735206546
Mean63.92590692
Median Absolute Deviation (MAD)8
Skewness0.3777961027
Sum3612453
Variance134.6742426
MonotonicityNot monotonic
2021-06-15T23:34:45.483150image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
712542
 
3.1%
552441
 
2.9%
652226
 
2.7%
582135
 
2.6%
592119
 
2.6%
532106
 
2.5%
642099
 
2.5%
602003
 
2.4%
541964
 
2.4%
571807
 
2.2%
Other values (52)35068
42.2%
(Missing)26574
32.0%
ValueCountFrequency (%)
354
 
< 0.1%
3623
 
< 0.1%
3782
 
0.1%
3894
 
0.1%
3934
 
< 0.1%
40194
 
0.2%
41542
0.7%
4276
 
0.1%
43430
0.5%
44733
0.9%
ValueCountFrequency (%)
100424
0.5%
9720
 
< 0.1%
9599
 
0.1%
94384
0.5%
93150
 
0.2%
9286
 
0.1%
9186
 
0.1%
90126
 
0.2%
88177
0.2%
87107
 
0.1%

PCN
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct50
Distinct (%)0.1%
Missing26610
Missing (%)32.0%
Infinite0
Infinite (%)0.0%
Mean67.23499309
Minimum36
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2021-06-15T23:34:45.600178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile54
Q163
median68
Q372
95-th percentile79
Maximum100
Range64
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.799193
Coefficient of variation (CV)0.1159990154
Kurtosis1.432080524
Mean67.23499309
Median Absolute Deviation (MAD)4
Skewness-0.2549130608
Sum3797029
Variance60.82741145
MonotonicityNot monotonic
2021-06-15T23:34:45.703176image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
713734
 
4.5%
673464
 
4.2%
693294
 
4.0%
683256
 
3.9%
703179
 
3.8%
663157
 
3.8%
722964
 
3.6%
652755
 
3.3%
642505
 
3.0%
742493
 
3.0%
Other values (40)25673
30.9%
(Missing)26610
32.0%
ValueCountFrequency (%)
3651
 
0.1%
3761
 
0.1%
4257
 
0.1%
4369
 
0.1%
44158
0.2%
45110
 
0.1%
46104
 
0.1%
47380
0.5%
48281
0.3%
49172
0.2%
ValueCountFrequency (%)
100129
 
0.2%
9618
 
< 0.1%
90265
0.3%
87100
 
0.1%
86239
0.3%
84143
 
0.2%
83313
0.4%
82145
 
0.2%
81569
0.7%
80497
0.6%

ICQU
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct81
Distinct (%)1.3%
Missing77050
Missing (%)92.7%
Infinite0
Infinite (%)0.0%
Mean55.2235648
Minimum31.49
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2021-06-15T23:34:45.814177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum31.49
5-th percentile39.88
Q149
median55
Q362
95-th percentile69.14
Maximum88
Range56.51
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.389154823
Coefficient of variation (CV)0.1700208028
Kurtosis0.3242025053
Mean55.2235648
Median Absolute Deviation (MAD)6
Skewness0.2752671902
Sum333218.99
Variance88.15622828
MonotonicityNot monotonic
2021-06-15T23:34:45.912177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59439
 
0.5%
56370
 
0.4%
65363
 
0.4%
54347
 
0.4%
62317
 
0.4%
48246
 
0.3%
51199
 
0.2%
50181
 
0.2%
53169
 
0.2%
69157
 
0.2%
Other values (71)3246
 
3.9%
(Missing)77050
92.7%
ValueCountFrequency (%)
31.4910
 
< 0.1%
3225
< 0.1%
34.9139
< 0.1%
369
 
< 0.1%
36.7838
< 0.1%
3729
< 0.1%
3837
< 0.1%
38.6710
 
< 0.1%
3940
< 0.1%
39.6233
< 0.1%
ValueCountFrequency (%)
8841
 
< 0.1%
7731
 
< 0.1%
7648
 
0.1%
75.0266
0.1%
7554
 
0.1%
71.9717
 
< 0.1%
7042
 
0.1%
69.1449
 
0.1%
69157
0.2%
6889
0.1%

ICMA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct89
Distinct (%)1.5%
Missing77050
Missing (%)92.7%
Infinite0
Infinite (%)0.0%
Mean60.24383825
Minimum31
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2021-06-15T23:34:46.020180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile42
Q154
median60
Q368
95-th percentile83
Maximum88
Range57
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.4738818
Coefficient of variation (CV)0.1904573503
Kurtosis-0.1730887047
Mean60.24383825
Median Absolute Deviation (MAD)8
Skewness0.1454429348
Sum363511.32
Variance131.6499636
MonotonicityNot monotonic
2021-06-15T23:34:46.116177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68421
 
0.5%
65329
 
0.4%
60286
 
0.3%
55222
 
0.3%
62214
 
0.3%
58188
 
0.2%
59183
 
0.2%
54181
 
0.2%
52176
 
0.2%
61172
 
0.2%
Other values (79)3662
 
4.4%
(Missing)77050
92.7%
ValueCountFrequency (%)
3129
 
< 0.1%
3448
 
0.1%
359
 
< 0.1%
35.556
 
< 0.1%
3843
 
0.1%
3915
 
< 0.1%
39.125
 
< 0.1%
40.827
 
< 0.1%
41123
0.1%
4253
0.1%
ValueCountFrequency (%)
8854
 
0.1%
85.5333
 
< 0.1%
8583
0.1%
83140
0.2%
8252
 
0.1%
7976
0.1%
78.3617
 
< 0.1%
7850
 
0.1%
7770
0.1%
75.4111
 
< 0.1%

ICLE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct73
Distinct (%)1.2%
Missing77050
Missing (%)92.7%
Infinite0
Infinite (%)0.0%
Mean54.83833444
Minimum36
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2021-06-15T23:34:46.226150image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile43
Q150
median54.54
Q360
95-th percentile66
Maximum76
Range40
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.821105659
Coefficient of variation (CV)0.124385719
Kurtosis0.2161309478
Mean54.83833444
Median Absolute Deviation (MAD)4.54
Skewness0.1086876941
Sum330894.51
Variance46.52748241
MonotonicityNot monotonic
2021-06-15T23:34:46.329733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58492
 
0.6%
50472
 
0.6%
60445
 
0.5%
52372
 
0.4%
54358
 
0.4%
48328
 
0.4%
55304
 
0.4%
62277
 
0.3%
56199
 
0.2%
65172
 
0.2%
Other values (63)2615
 
3.1%
(Missing)77050
92.7%
ValueCountFrequency (%)
362
 
< 0.1%
3857
0.1%
4053
0.1%
40.125
 
< 0.1%
4199
0.1%
4240
< 0.1%
42.7632
 
< 0.1%
4399
0.1%
43.8542
0.1%
43.943
 
< 0.1%
ValueCountFrequency (%)
7638
 
< 0.1%
73.9831
 
< 0.1%
70.7417
 
< 0.1%
7053
 
0.1%
6924
 
< 0.1%
6799
0.1%
6652
 
0.1%
65172
0.2%
64.9133
 
< 0.1%
6369
0.1%

ICHI
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct13
Distinct (%)3.9%
Missing82749
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean48.59483582
Minimum40.42
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2021-06-15T23:34:46.428733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum40.42
5-th percentile42.46
Q146.47
median48.04
Q351.62
95-th percentile53.53
Maximum54
Range13.58
Interquartile range (IQR)5.15

Descriptive statistics

Standard deviation3.997747781
Coefficient of variation (CV)0.08226692639
Kurtosis-1.118160045
Mean48.59483582
Median Absolute Deviation (MAD)3.58
Skewness-0.3544014483
Sum16279.27
Variance15.98198732
MonotonicityNot monotonic
2021-06-15T23:34:46.521733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
46.4744
 
0.1%
5041
 
< 0.1%
51.6241
 
< 0.1%
42.8839
 
< 0.1%
53.5338
 
< 0.1%
48.0438
 
< 0.1%
5336
 
< 0.1%
42.4617
 
< 0.1%
4812
 
< 0.1%
449
 
< 0.1%
Other values (3)20
 
< 0.1%
(Missing)82749
99.6%
ValueCountFrequency (%)
40.426
 
< 0.1%
41.175
 
< 0.1%
42.4617
 
< 0.1%
42.8839
< 0.1%
449
 
< 0.1%
46.4744
0.1%
4812
 
< 0.1%
48.0438
< 0.1%
5041
< 0.1%
51.6241
< 0.1%
ValueCountFrequency (%)
549
 
< 0.1%
53.5338
< 0.1%
5336
< 0.1%
51.6241
< 0.1%
5041
< 0.1%
48.0438
< 0.1%
4812
 
< 0.1%
46.4744
0.1%
449
 
< 0.1%
42.8839
< 0.1%

ICGE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct13
Distinct (%)3.9%
Missing82749
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean47.25259701
Minimum33.16
Maximum60.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2021-06-15T23:34:46.613706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum33.16
5-th percentile36.15
Q142.68
median47.24
Q349.14
95-th percentile60.66
Maximum60.66
Range27.5
Interquartile range (IQR)6.46

Descriptive statistics

Standard deviation6.495615896
Coefficient of variation (CV)0.1374657967
Kurtosis0.244834236
Mean47.25259701
Median Absolute Deviation (MAD)2.59
Skewness0.4342425573
Sum15829.62
Variance42.19302587
MonotonicityNot monotonic
2021-06-15T23:34:46.685706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
4977
 
0.1%
44.6544
 
0.1%
49.1441
 
< 0.1%
42.6839
 
< 0.1%
60.6638
 
< 0.1%
47.2428
 
< 0.1%
36.1517
 
< 0.1%
3912
 
< 0.1%
38.7410
 
< 0.1%
569
 
< 0.1%
Other values (3)20
 
< 0.1%
(Missing)82749
99.6%
ValueCountFrequency (%)
33.166
 
< 0.1%
36.1517
 
< 0.1%
38.7410
 
< 0.1%
3912
 
< 0.1%
429
 
< 0.1%
42.6839
< 0.1%
44.6544
0.1%
46.815
 
< 0.1%
47.2428
 
< 0.1%
4977
0.1%
ValueCountFrequency (%)
60.6638
< 0.1%
569
 
< 0.1%
49.1441
< 0.1%
4977
0.1%
47.2428
 
< 0.1%
46.815
 
< 0.1%
44.6544
0.1%
42.6839
< 0.1%
429
 
< 0.1%
3912
 
< 0.1%

ICFL
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct85
Distinct (%)1.4%
Missing77050
Missing (%)92.7%
Infinite0
Infinite (%)0.0%
Mean47.91555519
Minimum17.7
Maximum84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2021-06-15T23:34:46.784706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum17.7
5-th percentile33
Q142
median47.37
Q354
95-th percentile63.68
Maximum84
Range66.3
Interquartile range (IQR)12

Descriptive statistics

Standard deviation10.1344031
Coefficient of variation (CV)0.2115054926
Kurtosis0.9082644637
Mean47.91555519
Median Absolute Deviation (MAD)6.09
Skewness0.1416438573
Sum289122.46
Variance102.7061263
MonotonicityNot monotonic
2021-06-15T23:34:46.887706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53477
 
0.6%
44299
 
0.4%
51254
 
0.3%
43242
 
0.3%
42214
 
0.3%
45214
 
0.3%
46205
 
0.2%
61190
 
0.2%
55180
 
0.2%
34154
 
0.2%
Other values (75)3605
 
4.3%
(Missing)77050
92.7%
ValueCountFrequency (%)
17.742
 
0.1%
2449
 
0.1%
24.087
 
< 0.1%
2558
 
0.1%
2830
 
< 0.1%
28.310
 
< 0.1%
30103
0.1%
3388
0.1%
34154
0.2%
352
 
< 0.1%
ValueCountFrequency (%)
8441
 
< 0.1%
7748
 
0.1%
705
 
< 0.1%
6644
 
0.1%
65.2415
 
< 0.1%
65122
0.1%
63.6831
 
< 0.1%
6352
0.1%
62112
0.1%
61.8341
 
< 0.1%

ICFI
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct84
Distinct (%)1.4%
Missing77050
Missing (%)92.7%
Infinite0
Infinite (%)0.0%
Mean57.18990554
Minimum31
Maximum107
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2021-06-15T23:34:47.001706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile41
Q150
median56
Q362
95-th percentile79.203
Maximum107
Range76
Interquartile range (IQR)12

Descriptive statistics

Standard deviation11.70212682
Coefficient of variation (CV)0.2046187471
Kurtosis3.205279424
Mean57.18990554
Median Absolute Deviation (MAD)6
Skewness1.242947509
Sum345083.89
Variance136.9397721
MonotonicityNot monotonic
2021-06-15T23:34:47.108706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62579
 
0.7%
53494
 
0.6%
59365
 
0.4%
50307
 
0.4%
52247
 
0.3%
57246
 
0.3%
66225
 
0.3%
56205
 
0.2%
47193
 
0.2%
55152
 
0.2%
Other values (74)3021
 
3.6%
(Missing)77050
92.7%
ValueCountFrequency (%)
3125
< 0.1%
3360
0.1%
36.626
 
< 0.1%
37.538
< 0.1%
37.525
 
< 0.1%
3841
< 0.1%
3915
 
< 0.1%
4057
0.1%
4161
0.1%
42.1342
0.1%
ValueCountFrequency (%)
10752
0.1%
9941
 
< 0.1%
9318
 
< 0.1%
8778
0.1%
8653
0.1%
8043
0.1%
79.5817
 
< 0.1%
7948
0.1%
7641
 
< 0.1%
74106
0.1%

ICBI
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct83
Distinct (%)1.4%
Missing77050
Missing (%)92.7%
Infinite0
Infinite (%)0.0%
Mean54.00258535
Minimum28.18
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2021-06-15T23:34:47.222706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum28.18
5-th percentile41
Q148.95
median53
Q359
95-th percentile69.14
Maximum76
Range47.82
Interquartile range (IQR)10.05

Descriptive statistics

Standard deviation8.247325015
Coefficient of variation (CV)0.1527209292
Kurtosis0.002917575164
Mean54.00258535
Median Absolute Deviation (MAD)5
Skewness0.2564556969
Sum325851.6
Variance68.01836991
MonotonicityNot monotonic
2021-06-15T23:34:47.326706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51502
 
0.6%
53369
 
0.4%
57357
 
0.4%
55249
 
0.3%
64214
 
0.3%
54204
 
0.2%
58194
 
0.2%
48185
 
0.2%
45177
 
0.2%
62166
 
0.2%
Other values (73)3417
 
4.1%
(Missing)77050
92.7%
ValueCountFrequency (%)
28.185
 
< 0.1%
3315
 
< 0.1%
36.286
 
< 0.1%
36.3244
 
0.1%
3779
0.1%
387
 
< 0.1%
3969
0.1%
407
 
< 0.1%
41156
0.2%
4252
 
0.1%
ValueCountFrequency (%)
7639
< 0.1%
7544
0.1%
7259
0.1%
71.980
0.1%
7132
 
< 0.1%
69.6533
< 0.1%
69.1428
 
< 0.1%
6910
 
< 0.1%
68.8744
0.1%
6841
< 0.1%

ICSO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct70
Distinct (%)1.2%
Missing77385
Missing (%)93.1%
Infinite0
Infinite (%)0.0%
Mean52.57265661
Minimum5
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2021-06-15T23:34:47.431706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile38
Q146.81
median53
Q359
95-th percentile66.87
Maximum72
Range67
Interquartile range (IQR)12.19

Descriptive statistics

Standard deviation9.904179007
Coefficient of variation (CV)0.1883903087
Kurtosis3.624343329
Mean52.57265661
Median Absolute Deviation (MAD)6
Skewness-0.9366627911
Sum299611.57
Variance98.09276181
MonotonicityNot monotonic
2021-06-15T23:34:47.531706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56540
 
0.6%
50342
 
0.4%
63310
 
0.4%
54284
 
0.3%
61242
 
0.3%
47212
 
0.3%
46185
 
0.2%
41173
 
0.2%
38164
 
0.2%
42156
 
0.2%
Other values (60)3091
 
3.7%
(Missing)77385
93.1%
ValueCountFrequency (%)
555
 
0.1%
32.886
 
< 0.1%
335
 
< 0.1%
34.1936
 
< 0.1%
3662
 
0.1%
3757
 
0.1%
37.4442
 
0.1%
38164
0.2%
3951
 
0.1%
41173
0.2%
ValueCountFrequency (%)
72147
0.2%
717
 
< 0.1%
69.6317
 
< 0.1%
69.3431
 
< 0.1%
6913
 
< 0.1%
6859
0.1%
66.8749
 
0.1%
66.8511
 
< 0.1%
66141
0.2%
65.6810
 
< 0.1%

Interactions

2021-06-15T23:33:00.640614image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-15T23:33:00.765614image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-15T23:33:00.908615image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-15T23:33:03.161674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-15T23:33:03.280715image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-15T23:33:03.396705image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-15T23:33:03.510706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-06-15T23:34:47.891706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-06-15T23:34:48.132716image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-06-15T23:34:48.389853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-06-15T23:34:48.655854image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

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A simple visualization of nullity by column.
2021-06-15T23:34:40.658614image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-06-15T23:34:41.196615image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-06-15T23:34:41.664612image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

SUBJ_DESCCRSE_NUMBERGRDE_CODE_FINALCRN_KEYCRED_ACADCOLL_DESCMAJR_DESC1CAMPUSIND_REPBECA_GOB_ICFESHAVE_BECAHAVE_PHONEPSCPMAPLCPINPCNICQUICMAICLEICHIICGEICFLICFIICBIICSO
0ADMINISTRACION1093.8443622.0FAC DE INGEN ADMINISTRATIVASINGENIERIA DE MERCADOSCCNaNNOTIENE00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1ADMINISTRACION1264.7357532.0FAC DE INGEN ADMINISTRATIVASINGENIERIA DE MERCADOSCCNaNNOTIENE00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2ADMINISTRACION1264.5357532.0FAC DE INGEN ADMINISTRATIVASINGENIERIA DE MERCADOSCCNaNNOTIENE01NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3ADMINISTRACION1264.2357532.0FAC DE INGEN ADMINISTRATIVASINGENIERIA DE MERCADOSCCNaNNOTIENE00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4ADMINISTRACION160014.5418844.0FAC DE INGEN ADMINISTRATIVASINGENIERIA DE MERCADOSCCNaNNOTIENE00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
5ADMINISTRACION1094.3443622.0FAC DE INGEN ADMINISTRATIVASINGENIERIA DE MERCADOSCCNaNNOTIENE01NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
6ADMINISTRACION1264.7357532.0FAC DE INGEN ADMINISTRATIVASINGENIERIA DE MERCADOSCCNaNNOTIENE00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
7ADMINISTRACION1093.0443622.0FAC DE INGEN ADMINISTRATIVASINGENIERIA DE MERCADOSCCNaNNOTIENE00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
8ADMINISTRACION1264.5357532.0FAC DE INGEN ADMINISTRATIVASINGENIERIA DE MERCADOSCCNaNNOTIENE01NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9ADMINISTRACION1093.6443622.0FAC DE INGEN ADMINISTRATIVASINGENIERIA DE MERCADOSCCNaNNOTIENE01NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN

Last rows

SUBJ_DESCCRSE_NUMBERGRDE_CODE_FINALCRN_KEYCRED_ACADCOLL_DESCMAJR_DESC1CAMPUSIND_REPBECA_GOB_ICFESHAVE_BECAHAVE_PHONEPSCPMAPLCPINPCNICQUICMAICLEICHIICGEICFLICFIICBIICSO
83074TERMOFLUIDOS180014.5469993.0FAC DE INGENIERIAINGENIERIA MECATRONICACCINOTIENE0054.064.068.050.064.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
83075TERMOFLUIDOS180013.5469993.0FAC DE INGENIERIAINGENIERIA MECATRONICACCNaNNOTIENE0163.062.064.066.065.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
83076TERMOFLUIDOS180023.3478693.0FAC DE INGENIERIAINGENIERIA MECATRONICACCNaNNOTIENE0072.076.079.083.068.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
83077TERMOFLUIDOS180013.6469993.0FAC DE INGENIERIAINGENIERIA MECATRONICACCIBI16 - PLAN 10000 BECAS GOBIERNO 2016, BM16 - MATRICULA 10000 BECAS GOB 20161167.075.070.072.067.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
83078TERMOFLUIDOS180023.1478853.0FAC DE INGENIERIAINGENIERIA MECATRONICACCNaNBI17 - PLAN 10000 BECAS GOBIERNO 2017, BM17 - MATRICULA 10000 BECAS GOB 20171174.075.072.083.071.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
83079TERMOFLUIDOS180023.5478853.0FAC DE INGENIERIAINGENIERIA MECATRONICACCNaNNOTIENE0148.066.059.056.053.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
83080TERMOFLUIDOS180012.4469993.0FAC DE INGENIERIAINGENIERIA MECATRONICACCNaNBI17 - PLAN 10000 BECAS GOBIERNO 2017, BM17 - MATRICULA 10000 BECAS GOB 20171065.077.072.059.071.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
83081TERMOFLUIDOS180024.2505723.0FAC DE INGENIERIAINGENIERIA MECATRONICACCNaNNOTIENE0060.067.063.069.066.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
83082TERMOFLUIDOS180013.7469993.0FAC DE INGENIERIAINGENIERIA MECATRONICACCNaNBI16 - PLAN 10000 BECAS GOBIERNO 2016, BM16 - MATRICULA 10000 BECAS GOB 20161065.073.078.074.075.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
83083TERMOFLUIDOS180024.0478693.0FAC DE INGENIERIAINGENIERIA MECATRONICACCNaNBI18 - GENERACION E BECAS GOB 2018, BM18 - MATRICULA GEN E BECAS GOB 20181167.083.070.078.074.0NaNNaNNaNNaNNaNNaNNaNNaNNaN